Real-Time FRAML at Scale: Turning Payment Risk Into Incremental Revenue

Background

A large payments infrastructure provider powers card-based transaction processing for a broad ecosystem of downstream fintechs and financial institutions. As their customer base grew, so did the urgency: fraud patterns shifted faster, clients demanded quicker responses, and internal teams were expected to protect more volume with the same headcount.

At the same time, leadership saw a strategic opportunity. If they could modernize fraud decisioning and package it as a white-labeled platform, risk would stop being a cost center alone, and start becoming a differentiated, revenue-generating service for their customers.

Challenge

The team’s reality, however, was that the fraud program was running on a homegrown system that couldn’t keep up with modern attacks. 

When clients identified emerging fraud, there was no way to solve it fast. Each delay stretched the window of exposure and increased pressure from customers.

Operating in a low-margin environment, they needed to protect revenue while controlling operating costs and meeting growing compliance expectations.

As the business scaled, four issues threatened their roadmap:

• Limited agility in their in-house Fraud & AML stack, making it difficult to keep pace with new threats.
  
• No merchant-facing FRAML offering, which constrained their ability to extend fraud/AML capabilities to clients as a value-added service (and potential revenue stream).
  
• Real-time, high-throughput requirements, requiring a scalable decisioning engine that could handle millions of daily transactions with ultra-low latency.
  
• Rising compliance and audit pressure, demanding fully auditable, transparent processes to reduce regulatory exposure.
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Solutions

To move from reactive, fragmented controls to a scalable program built for real-time payments, the organization selected DataVisor to consolidate fraud and AML into a single, unified system.

• Unified, end-to-end and real-time fraud and AML platform to centralize workflows and reduce operational complexity across teams.
  
• No/low-code strategy development so risk teams could deploy and update rules and logic rapidly, without heavy engineering dependence.
  
• Built-in multi-tenancy to enable packaging and reselling fraud and AML services to merchant clients as a value-added offering.
  
• Ensemble machine learning (supervised + unsupervised): improved proactive detection for evolving threats beyond purely rules-based approaches. 
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Results

After deployment, the organization shifted from a slow, reactive fraud posture to a model built for speed and scale, tightening protection while improving the customer experience of risk operations.

Business Impact

Fast Time-to-Revenue

Onboard new customers in less than 1 day with multi-tenancy capabilities, scaling to 40+ subtenants.